1. Field
This disclosure is generally related to recommender systems. More specifically, this disclosure is related to using encoded or obfuscated ratings information to perform collaborative filtering.
2. Related Art
Online services often use recommender systems to predict items that a user is likely to be interested in, or to predict how the user is likely to rate the item. These recommender systems can improve the user's experience as he visits an online retailer, searches for a streaming movie to watch, or otherwise peruses any other type of user-rated online content. The accuracy of a recommender system can allow the online service to present the user with items that the user is likely to be interested in, but oftentimes at the cost of sacrificing the user's privacy.
Through recommender systems, the user exchanges a transcript of his purchasing and rating history to get relevant recommendations for items such as movies, restaurants, books, hotels, travel, and the like. In practice, recommender systems can have a sparse dataset of rating information (e.g., a movie rating dataset from Netflix, Inc.), where individual users often provide a rating for less than 1% of all items. These ratings are often related to items that the user is interested in. Therefore, because it can be common for a user to not have rated an item, the recommender system can obtain sensitive information about the user based on the items purchased or rated by the user. The recommender system may be able to infer sensitive information about the user based on the types of items the user has rated, and their rating values (e.g., types of movies the user has watched and liked or disliked).
Moreover, the recommender system can perform collaborative filtering to make a recommendation for the user based on the assumption that the users who agreed in the past are likely to agree in the future. Thus, the recommender system can make sensitive inferences about the user based on the purchasing or rating behavior made by other users that have agreed with this user in the past.
Unfortunately, recommender systems do not implement sufficient safeguards to protect the user information in the case that the user's purchasing and rating information is released unintentionally. For example, if the recommender system becomes compromised (e.g., by a malicious user or a government subpoena), the malicious user is able to expose the user's preferences from the purchasing and ratings behavior. Further, the malicious user can use the ratings information for a plurality of users to infer, with a high probability, how each user is likely to rate other items.